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@BOOK{Anderson2000,
  title = {Learning and Memory},
  publisher = {Wiley},
  year = {2000},
  editor = {Ellen Schatz},
  author = {John R. Anderson},
  edition = {second},
  isbn = {0-471-24925-4},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOKLET{Boomgaard2007,
  title = {Machine vision - an introduction for computer scientists},
  author = {Rein van den Boomgaard and Leo Dorst},
  year = {2007},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Bratko2001,
  title = {Prolog - Programming for Artificial Intelligence},
  publisher = {Pearson Edication},
  year = {2001},
  editor = {A.D. McGettrick},
  author = {Ivan Bratko},
  series = {International computer science series},
  edition = {third},
  isbn = {0-201-40375-7},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Bretscher2005,
  title = {Linear algebra with applications},
  publisher = {Prentice Hall},
  year = {2005},
  author = {Otto Bretscher},
  address = {Upper Saddle River, New Jersey},
  edition = {third},
  isbn = {0-13-127772-3},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Bryant2003,
  title = {Computer systems - a programmer's perspective},
  publisher = {Prentice Hall},
  year = {2003},
  author = {Randal E. Bryant and David R. O'Hallaron},
  address = {Upper Saddle River, New Jersey},
  isbn = {0-13-034074-X},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@ARTICLE{Desain2003,
  author = {Peter Desain and Henkjan Honing},
  title = {The formation of rhythmic categories and metric priming},
  journal = {Perception},
  year = {2003},
  volume = {32},
  pages = {341--365},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@ARTICLE{Desain1999,
  author = {Peter Desain and Henkjan Honing},
  title = {Computational Models of Beat Induction: The Rule-Based Approach},
  journal = {Journal of New Music Research},
  year = {1999},
  volume = {28},
  pages = {29--42},
  number = {1},
  abstract = {This paper is a report of ongoing research on the computational modeling
	of beat induction which aims at chieving a better understanding of
	the perceptual processes involved by ordering and reformulating existing
	models. One family of rule-based beat induction models is described
	(Longuet-Higgins and Lee, 1982; Lee, 1985; Longuet-Higgins, 1994),
	along with the presentation of analysis methods that allow an evaluation
	of the models in terms of their in- and output spaces, abstracting
	from internal detail. It builds on work described in (Desain and
	Honing, 1994b). The present paper elaborates these methods and presents
	the results obtained. It will be shown that they can be used to characterize
	the differences between these models, a point that was difficult
	to assess previously. Furthermore, the first results of using the
	method to improve the existing rule-based models are presented, by
	describing the most effective version of a specific rule, and the
	most effective parameter settings.},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@UNPUBLISHED{Dorst2007,
  author = {Leo Dorst},
  title = {Image Transformations in Homogeneous Coordinates},
  note = {Part of Boomgaard2008},
  month = {January},
  year = {2007},
  abstract = {When you do image processing or computer graphics, you naturally have
	to deal with motions and projective views of moved elements. In standard
	linear algebra, rotations are easy to treat with orthogonal matrices,
	but translations are not even linear operations. Therefore standard
	3D linear algebra is not a complete tool for the treatment of motions
	and projections in 3D. However, there is a neat trick: by embedding
	3D space in a 4D representational space, the linear algebra of that
	4D space is precisely what we need to do projections and motions
	in the 3D space, and they even become unified as the same kind of
	operation. This trick is known as homogeneous coordinates (we’ll
	find out why). The same trick can be used in one dimension less,
	to conveniently compute with projective distortions in 2D images
	by using the linear algebra of a 3D representative space. We treat
	homogeneous coordinates in that form, and show how they permit you
	to use standard numerical linear algebra techniques to find transformations.},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOKLET{Dorst2001,
  title = {An Introduction to Robotics for the Computer Sciences},
  author = {Leo Dorst},
  month = {March},
  year = {2001},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Goodrich2006,
  title = {Data Structures \& Algorithms in Java},
  publisher = {Wiley},
  year = {2006},
  author = {Michael T. Goodrich and Roberto Tamassia},
  edition = {fourth},
  isbn = {0-471-73884-0},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Jurafsky2000,
  title = {Speech and Language Processing},
  publisher = {Prentice Hall},
  year = {2000},
  editor = {Stuart Russell and Peter Norvig},
  author = {Daniel Jurafsky and James H. Martin},
  series = {Prentice Hall series in Artificial Intelligence},
  address = {Upper Saddle River, New Jersey},
  isbn = {0-13-122798-X},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Kernighan1988,
  title = {The {C} Programming Language, Second Edition},
  publisher = {Prentice-Hall},
  year = {1988},
  author = {Brian W. Kernighan and Dennis M. Ritchie},
  address = {Englewood Cliffs, New Jersey},
  isbn = {0-13-110362-8},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@ARTICLE{Krumhansl2000,
  author = {Carol L. Krumhansl},
  title = {Rhythm and Pitch in Music Cognition},
  journal = {Psychological Bulletin},
  year = {2000},
  volume = {126},
  pages = {159--179},
  number = {1},
  abstract = {Rhythm and pitch are the 2 primary dimensions of music. They are interesting
	psychologically because simple, well-defined units combine to form
	highly complex and varied patterns. This article brings together
	the major developments in research on how these dimensions are perceived
	and remembered, beginning with psychophysical results on time and
	pitch perception. Progressively larger units are considered, moving
	from basic psychological categories of temporal and frequency ratios,
	to pulse and scale, to metrical and tonal hierarchies, to the formation
	of musical rhythms and melodies, and finally to the cognitive representation
	of large-scale musical form. Interactions between the dimensions
	are considered, and major theoretical proposals are described. The
	article identifies various links between musical structure and perceptual
	and cognitive processes, suggesting psychological influences on how
	sounds are patterned in music.},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@ARTICLE{Large1994,
  author = {Edward W. Large and John F. Kolen},
  title = {Resonance and the Perception of Musical Meter},
  journal = {Connection Science},
  year = {1994},
  volume = {6},
  pages = {177--208},
  number = {2},
  abstract = {Many connectionist approaches to musical expectancy and music composition
	let the question of 'What next?' overshadow the equally important
	question of 'When next?'. One cannot escape the latter question,
	one of remporal structure, when considering the perception of musical
	meter. We view the perception of mem'cal structure as a dynamic process
	where the temporal organization of external musical events synchronizes,
	or entrains, a listener's internal processing mechanisms. This article
	introduces a novel connectionist unit, based upon a mathematical
	model of entrainment, capable of phase- and frequency-locking to
	periodic components of incoming rhythmic patterns. Networks of these
	units can self-organize temporally structured responses to rhythmic
	patterns. The resulting network behavior embodies the perception
	of mem'cal structure. The article concludes with a discussion of
	the implications of our approach for theories of memcal structure
	and musical expectancy.},
  doi = {10.1080/09540099408915723},
  keywords = {Beat, Meter, Metrical structure, Entrainment, Phase-locking, Beat-tracking,
	Meter perception},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03},
  url = {http://dx.doi.org/10.1080/09540099408915723}
}

@ARTICLE{Lidz2003,
  author = {Jeffrey Lidz and Sandra Waxman and Jennifer Freedman},
  title = {What infants know about syntax but couldn’t have learned: experimental
	evidence for syntactic structure at 18 months},
  journal = {Cognition},
  year = {2003},
  volume = {89},
  pages = {B65–-B73},
  abstract = {Generative linguistic theory stands on the hypothesis that grammar
	cannot be acquired solely on the basis of an analysis of the input,
	but depends, in addition, on innate structure within the learner
	to guide the process of acquisition. This hypothesis derives from
	a logical argument, however, and its consequences have never been
	examined experimentally with infant learners. Challenges to this
	hypothesis, claiming that an analysis of the input is indeed sufficient
	to explain grammatical acquisition, have recently gained attention.
	We demonstrate with novel experimentation the insufficiency of this
	countervailing view. Focusing on the syntactic structures required
	to determine the antecedent for the pronoun one, we demonstrate that
	the input to children does not contain sufficient information to
	support unaided learning. Nonetheless, we show that 18-month-old
	infants do have command of the syntax of one. Because this syntactic
	knowledge could not have been gleaned exclusively from the input,
	infants’ mastery of this aspect of syntax constitutes evidence for
	the contribution of innate structure within the learner in acquiring
	a grammar.},
  keywords = {Universal Grammar, Poverty of the stimulus,, Language acquisition},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@ARTICLE{Longuet-Higgins1979,
  author = {H. C. Longuet-Higgins},
  title = {The perception of music},
  journal = {Proceedings of the Royal Society},
  year = {1979},
  volume = {B 205},
  pages = {307-322},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@ARTICLE{Longuet-Higgins1982,
  author = {H. Christopher Longuet-Higgins and Christopher S. Lee},
  title = {The perception of musical rhythms},
  journal = {Perception},
  year = {1982},
  volume = {11},
  pages = {115-128},
  abstract = {There are many musical sequences in which the rhythm is evident from
	the mere durations of the notes. A simple theory is proposed of how
	a listener may infer the rhythm of such a sequence by comparing the
	note length. It is assumed that the listener forms an idea of the
	rhythm as a sequence unfolds, constructing and eliminating metrical
	hypotheses in the light of what he hears. The theory, which differs
	in some important respects from earlier proposals, has been implemented
	as a computer program. The program has been tested on a wide variety
	of musical examples, and its successes and failures are discussed
	in detail.},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Mitchell1997,
  title = {Machine Learning},
  publisher = {McGraw-Hill International Editions},
  year = {1997},
  editor = {C. L. Lui and Allen B. Tucker},
  author = {Tom M. Mitchell},
  isbn = {0-07-042807-7},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Murphy2000,
  title = {Introduction to AI robotics},
  publisher = {The MIT Press},
  year = {2000},
  editor = {Ronald C. Arkin},
  author = {Robin R. Murphy},
  series = {Intelligent Robots and Autonomous Agents},
  isbn = {0-262-13383-0},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Preece2002,
  title = {Interaction design},
  publisher = {Wiley},
  year = {2002},
  author = {Jennifer Preece and Yvonne Rogers and Helen Sharp},
  isbn = {0-471-49278-7},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@ARTICLE{Regier2004,
  author = {Terry Regier and Susanne Gahl},
  title = {Learning the unlearnable: the role of missing evidence},
  journal = {Cognition},
  year = {2004},
  volume = {93},
  pages = {147--155},
  abstract = {Syntactic knowledge is widely held to be partially innate, rather
	than learned. In a classic example, it is sometimes argued that children
	know the proper use of anaphoric one, although that knowledge could
	not have been learned from experience. Lidz et al. [Lidz, J., Waxman,
	S., & Freedman, J. (2003). What infants know about syntax but couldn’t
	have learned: Experimental evidence for syntactic structure at 18
	months. Cognition, 89, B65– B73.] pursue this argument, and present
	corpus and experimental evidence that appears to support it; they
	conclude that specific aspects of this knowledge must be innate.
	We demonstrate, contra Lidz et al., that this knowledge may in fact
	be acquired from the input, through a simple Bayesian learning procedure.
	The learning procedure succeeds because it is sensitive to the absence
	of particular input patterns—an aspect of learning that is apparently
	overlooked by Lidz et al. More generally, we suggest that a prominent
	form of the “argument from poverty of the stimulus” suffers from
	the same oversight, and is as a result logically unsound.},
  keywords = {Language acquisition, Syntax, Innateness, Poverty of the stimulus,
	Emergence, Bayesian learning, Indirect learning},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Russel2003,
  title = {Artificial Intelligence - a modern approach},
  publisher = {Prentice Hall},
  year = {2003},
  author = {Stuart J. Russel and Peter Norvig},
  series = {Prentice Hall series in Artificial Intelligence},
  address = {Upper Saddle River, New Jersey},
  edition = {second},
  isbn = {0-13-080302-2},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Seibel2005,
  title = {Practical {Common Lisp}},
  publisher = {Apress},
  year = {2005},
  author = {Peter Seibel},
  pages = {xxv + 499},
  address = {Berkeley, CA, USA},
  isbn = {1-59059-239-5},
  owner = {Patrick de Kok},
  subject = {COMMON LISP (Computer program language)},
  timestamp = {2008.02.03},
  url = {http://www.gigamonkeys.com/book/}
}

@BOOK{Silberschatz2006,
  title = {Database system concepts},
  publisher = {McGraw-Hill International Editions},
  year = {2006},
  author = {Abraham Silberschatz and Henry F. Korth and S. Sudarshan},
  edition = {fifth},
  isbn = {007-124476-X},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@BOOK{Sternberg2006,
  title = {Cognitive Psychology},
  publisher = {Thomson Wadsworth},
  year = {2006},
  editor = {Marianne Taflinger},
  author = {Robert J. Sternberg},
  edition = {fourth},
  isbn = {0-495-00699-8},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@book{Theodoridis2002,
  title = {Pattern Recognition},
  publisher = {{Academic Press}},
  year = {2002},
  author = {Sergios Theodoridis and Konstantinos Koutroumbas},
  edition = {second},
  address = {San Diego, CA, USA},
  howpublished = {Hardcover},
  keywords = {bibtex-import, pattern\_recognition},
  isbn = {0-12-685875-6},
  owner = {Patrick de Kok},
  timestamp = {2008.02.21}
}

@INPROCEEDINGS{Zaanen2003,
  author = {Menno van Zaanen and Rens Bod and Henkjan Honing},
  title = {A memory-based approach to meter induction},
  booktitle = {Proceedings of the 5th Triennial ESCOM Conference},
  year = {2003},
  pages = {250--253},
  abstract = {Meter induction has been an important topic in the computational modeling
	of music cognition for quite some time now. In this paper, an attempt
	is made to model how listeners arrive at a metrical interpretation
	of a fragment of music. A number of existing models are based on
	the Gestalt principles of perception, 'simplicity’ or ease of encoding
	being a key aspect. An alternative to this approach are models based
	on the notion of 'likelihood', so-called memory-based models. We
	adapt and evaluate a number of memory-based models for parsing metrical
	structure. More specifically, we will use the models covered by the
	Data-Oriented Parsing (DOP) framework. This framework defines a large
	class of probabilistic grammars that take sub-trees from an annotated
	corpus to form a general Probabilistic Tree Grammar. The models are
	tested on the National Anthems collection, yielding encouraging results.},
  isbn = {3-931852-67-9},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@INPROCEEDINGS{Zhang2004a,
  author = {Harry Zhang},
  title = {The optimality of naive Bayes},
  booktitle = {Proceedings of the Seventeenth Florida Artificial Intelligence Research
	Society Conference},
  year = {2004a},
  pages = {562--567},
  publisher = {The AAAI Press},
  abstract = {Naive Bayes is one of the most efficient and effective inductive learning
	algorithms for machine learning and data mining. Its competitive
	performance in classification is surprising, because the conditional
	independence assumption on which it is based, is rarely true in real-world
	applications. An open question is: what is the true reason for the
	surprisingly good performance of naive Bayes in classification?
	
	In this paper, we propose a novel explanation on the superb classification
	performance of naive Bayes. We show that, essentially, the dependence
	distribution; i.e., how the local dependence of a node distributes
	in each class, evenly or unevenly, and how the local dependencies
	of all nodes work together, consistently (supporting a certain classification)
	or inconsistently (canceling each other out), plays a crucial role.
	Therefore, no matter how strong the dependences among attributes
	are, naive Bayes can still be optimal if the dependences distribute
	evenly in classes, or if the dependences cancel each other out. We
	propose and prove a sufficient and necessary conditions for the optimality
	of naive Bayes. Further, we investigate the optimality of naive Bayes
	under the Gaussian distribution. We present and prove a sufficient
	condition for the optimality of naive Bayes, in which the dependence
	between attributes do exist. This provides evidence that dependence
	among attributes may cancel out each other. In addition, we explore
	when naive Bayes works well.},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03},
  url = {http://www.cs.unb.ca/profs/hzhang/publications/FLAIRS04ZhangH.pdf}
}

@TECHREPORT{Zhang1998,
  author = {Zhengyou Zhang},
  title = {A Flexible New Technique for Camera Calibration},
  institution = {Microsoft Research},
  year = {1998},
  address = {One Microsoft Way, Redmond},
  month = {December},
  abstract = {We propose a flexible new technique to easily calibrate a camera.
	It is well suited for use without specialized knowledge of 3D geometry
	or computer vision. The technique only requires the camera to observe
	a planar pattern shown at a few (at least two) different orientations.
	Either the camera or the planar pattern can be freely moved. The
	motion need not be known. Radial lens distortion is modeled. The
	proposed procedure consists of a closed-form solution, followed by
	a nonlinear refinement based on the maximum likelihood criterion.
	Both computer simulation and real data have been used to test the
	proposed technique, and very good results have been obtained. Compared
	with classical techniques which use expensive equipment such as two
	or three orthogonal planes, the proposed technique is easy to use
	and flexible. It advances 3D computer vision one step from laboratory
	environments to real world use.},
  keywords = {Camera calibration, calibration from planes, 2D pattern, absolute
	conic, projective mapping, lens distortion, closed-form solution,
	maximum likelihood estimation, flexible setup.},
  owner = {Patrick de Kok},
  timestamp = {2008.02.03}
}

@comment{jabref-meta: selector_journal:}

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@comment{jabref-meta: selector_keywords:Bayesian learning;Beat;Beat-tr
acking;Emergence;Entrainment;Indirect learning;Innateness;Language acq
uisition;Meter;Meter perception;Metrical structure;Phase-locking;Pover
ty of the stimulus;Syntax;Universal grammar;}

@comment{jabref-meta: selector_publisher:}

